Prediction of Surface Water Supply Sources for the District of Columbia Using Least Squares Support Vector Machines (LS-SVM) Method

In this research, we developed a predictive model based on least squares support vector machine (LS-SVM) that forecasts the future streamflow discharge using the paststreamflow discharge data. A Gaussian Radial Basis Function (RBF) kernel framework was built on the data set to tune the kernel parameters and regularization constantsof the model with respect to the given performance measure. The 10-fold cross-validation is used as a costfunction for estimating the performance of the model. The training process of LS-SVM was designed to train the support values and the bias term of an LS-SVM forfunction approximation. After the network has been well trained, we test the prediction performance on the newtesting samples, as well as the training samples. The USGSreal-time streamflow data were used as time series input. The experimental results showed that the proposed LSSVMalgorithm is a reliable and efficient method for streamflow prediction, which has an important impact to the water resource management field

کلیدواژه ها:

Water Quantity Prediction ، Least Squares Support vector Machine